Machine learning for large-scale optimization in 6g wireless networks
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
Multi-agent reinforcement learning for dynamic resource management in 6G in-X subnetworks
The 6G network enables a subnetwork-wide evolution, resulting in a “network of
subnetworks”. However, due to the dynamic mobility of wireless subnetworks, the data …
subnetworks”. However, due to the dynamic mobility of wireless subnetworks, the data …
Efficient reinforcement learning-based transmission control for mitigating channel congestion in 5G V2X sidelink
Channel congestion has been an open challenge for vehicular networks due to the limited
resource of communication channels. Explosion of channel access requests from a massive …
resource of communication channels. Explosion of channel access requests from a massive …
Intelligent spectrum sensing and access with partial observation based on hierarchical multi-agent deep reinforcement learning
Dynamic spectrum access (DSA) has been regarded as a viable solution to the spectrum
shortage problem. To find idle spectrum, partial spectrum sensing could be employed by …
shortage problem. To find idle spectrum, partial spectrum sensing could be employed by …
Federated quantum neural network with quantum teleportation for resource optimization in future wireless communication
B Narottama, SY Shin - IEEE Transactions on Vehicular …, 2023 - ieeexplore.ieee.org
The following study introduces FT-QNN, a federated and quantum teleportation–based
quantum neural network, utilized to optimize resource allocation for future wireless …
quantum neural network, utilized to optimize resource allocation for future wireless …
SWIPT-enabled Cell-Free Massive MIMO-NOMA Networks: A Machine Learning-based Approach
This paper investigates simultaneous wireless information and power transfer (SWIPT)-
enabled cell-free massive multiple-input multiple-output (CF-mMIMO) networks with power …
enabled cell-free massive multiple-input multiple-output (CF-mMIMO) networks with power …
Energy-efficient rate-splitting multiple access: A deep reinforcement learning-based framework
M Diamanti, G Kapsalis, EE Tsiropoulou… - IEEE Open Journal …, 2023 - ieeexplore.ieee.org
Rate-Splitting Multiple Access (RSMA) has been recognized as an effective technique to
reconcile the tradeoff between decoding interference and treating interference as noise in …
reconcile the tradeoff between decoding interference and treating interference as noise in …
Intelligent cloud-edge collaborations assisted energy-efficient power control in heterogeneous networks
L Zhang, J Peng, J Zheng… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
We consider a typical heterogeneous network (HetNet), which consists of a macro base
station (BS) and multiple small BSs sharing the same spectrum band. Since the spectrum …
station (BS) and multiple small BSs sharing the same spectrum band. Since the spectrum …
Multi-agent Reinforcement Learning based Distributed Channel Access for Industrial Edge-Cloud Web 3.0
In the emerging Web 3.0 applications for mass customized and personalized manufacturing,
smart mobile resources need to interact with each other and other resources to achieve …
smart mobile resources need to interact with each other and other resources to achieve …
Prior Knowledge-Augmented Broad Reinforcement Learning Framework for Fault Diagnosis of Heterogeneous Multiagent Systems
L Guo, Y Ren, R Li, B Jiang - IEEE Transactions on Cognitive …, 2023 - ieeexplore.ieee.org
A heterogeneous multiagent system (MAS) can easily experience unpredictable faults due
to its complex structure and involvement of different individuals. However, existing …
to its complex structure and involvement of different individuals. However, existing …